Examining Exploratory Trajectories for Minimizing Map Uncertainty

We examine the problem of minimizing uncertainty in the automated construction of a visual map of an unknown environment. Our work is motivated by the idea that a robot’s exploration policy can impact the accuracy of the resulting map, and we seek to examine the behavior of a set of policies that exhibit a trade-off between accuracy and efficiency. We are further motivated by the specific requirements of our map representation, which learns a set of implicit models of visual features. Such a representation precludes the instantiation of explicitly parameterized landmarks, such as those employed in standard concurrent mapping and localization frameworks. This paper examines a parameterized family of spiral trajectories in the plane and determines mapping accuracy as a function of the parameterization. We present experimental results demonstrating the map construction framework and discuss the implications for future work.